#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest

import numpy as np
from op_test import get_device_place, is_custom_device

import paddle
import paddle.nn.functional as F
from paddle import base


def p_normalize(x, axis=1, p=2, epsilon=1e-12, keepdims=True):
    xp = np.power(np.abs(x), p)
    s = np.sum(xp, axis=axis, keepdims=keepdims)
    r = np.maximum(np.power(s, 1.0 / p), epsilon)
    return x / r


class TestNNFunctionalNormalize(unittest.TestCase):
    def setUp(self):
        self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
        self.input_np2 = np.array([0.0, 0.0]).astype(np.float32)
        self.expected0 = p_normalize(self.input_np)
        self.expected1 = p_normalize(self.input_np, p=1.5)
        self.expected2 = p_normalize(self.input_np, axis=0)
        self.expected3 = p_normalize(self.input_np2, axis=0)

    def run_imperative(self):
        x = paddle.to_tensor(self.input_np)
        y = F.normalize(x)
        np.testing.assert_allclose(y.numpy(), self.expected0, rtol=1e-05)

        y = F.normalize(x, p=1.5)
        np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)

        y = F.normalize(x, axis=0)
        np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)

        x = paddle.to_tensor(self.input_np2)
        y = F.normalize(x, axis=0)
        np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)

        self.assertRaisesRegex(
            ValueError,
            r"Attr\(axis\) value should be in range \[-R, R-1\]",
            F.normalize,
            x,
        )

    def run_static(self, use_gpu=False):
        x = paddle.static.data(name='input', shape=[10, 10], dtype='float32')
        x2 = paddle.static.data(name='input2', shape=[2], dtype='float32')
        result0 = F.normalize(x)
        result1 = F.normalize(x, p=1.5)
        result2 = F.normalize(x, axis=0)
        result3 = F.normalize(x, name='aaa')
        result4 = F.normalize(x2, axis=0)

        place = get_device_place() if use_gpu else base.CPUPlace()
        exe = base.Executor(place)
        exe.run(paddle.static.default_startup_program())
        static_result = exe.run(
            feed={"input": self.input_np, "input2": self.input_np2},
            fetch_list=[result0, result1, result2, result4],
        )

        np.testing.assert_allclose(static_result[0], self.expected0, rtol=1e-05)
        np.testing.assert_allclose(static_result[1], self.expected1, rtol=1e-05)
        np.testing.assert_allclose(static_result[2], self.expected2, rtol=1e-05)
        np.testing.assert_allclose(static_result[3], self.expected3, rtol=1e-05)
        self.assertRaises(ValueError, F.normalize, x2)

    def test_cpu(self):
        paddle.disable_static(place=paddle.base.CPUPlace())
        self.run_imperative()
        paddle.enable_static()

        with paddle.static.program_guard(paddle.static.Program()):
            self.run_static()

    def test_gpu(self):
        if not (base.core.is_compiled_with_cuda() or is_custom_device()):
            return

        paddle.disable_static(place=get_device_place())
        self.run_imperative()
        paddle.enable_static()

        with paddle.static.program_guard(paddle.static.Program()):
            self.run_static(use_gpu=True)


class TestNormalizeAPI_Compatibility(unittest.TestCase):
    def setUp(self):
        np.random.seed(2025)
        self.places = ['cpu', get_device_place()]
        self.shape = [2, 3, 4]
        self.dtype = "float32"
        self.init_data()

    def init_data(self):
        self.np_x = np.random.rand(*self.shape).astype(self.dtype)
        self.p = 2
        self.axis = 1
        self.epsilon = 1e-12

    def test_dygraph_Compatibility(self):
        paddle.disable_static()
        x = paddle.to_tensor(self.np_x)
        paddle_dygraph_out = []
        # Position args (args)
        out1 = paddle.nn.functional.normalize(
            x, self.p, self.axis, self.epsilon
        )
        paddle_dygraph_out.append(out1)
        # Key words args (kwargs) for paddle
        out2 = paddle.nn.functional.normalize(
            x=x, p=self.p, axis=self.axis, epsilon=self.epsilon
        )
        paddle_dygraph_out.append(out2)
        # Key words args for torch compatibility
        out3 = paddle.nn.functional.normalize(
            input=x, p=self.p, dim=self.axis, eps=self.epsilon
        )
        paddle_dygraph_out.append(out3)
        # Key words args for out
        out4 = paddle.zeros_like(x)
        paddle.nn.functional.normalize(
            x, self.p, self.axis, self.epsilon, out=out4
        )
        paddle_dygraph_out.append(out4)
        # Numpy reference output
        ref_out = self.np_x / np.maximum(
            np.linalg.norm(
                self.np_x, ord=self.p, axis=self.axis, keepdims=True
            ),
            self.epsilon,
        )

        for out in paddle_dygraph_out:
            np.testing.assert_allclose(
                ref_out, out.numpy(), rtol=1e-05, atol=1e-08
            )
        paddle.enable_static()

    def test_static_Compatibility(self):
        paddle.enable_static()
        main = paddle.static.Program()
        startup = paddle.static.Program()
        with paddle.base.program_guard(main, startup):
            x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
            # Position args (args)
            out1 = paddle.nn.functional.normalize(
                x, self.p, self.axis, self.epsilon
            )
            # Key words args (kwargs) for paddle
            out2 = paddle.nn.functional.normalize(
                x=x, p=self.p, axis=self.axis, epsilon=self.epsilon
            )
            # Key words args for torch compatibility
            out3 = paddle.nn.functional.normalize(
                input=x, p=self.p, dim=self.axis, eps=self.epsilon
            )
            # Numpy reference output
            ref_out = self.np_x / np.maximum(
                np.linalg.norm(
                    self.np_x, ord=self.p, axis=self.axis, keepdims=True
                ),
                self.epsilon,
            )

            fetch_list = [out1, out2, out3]
            for place in self.places:
                exe = paddle.base.Executor(place)
                fetches = exe.run(
                    main,
                    feed={"x": self.np_x},
                    fetch_list=fetch_list,
                )
                for out in fetches:
                    np.testing.assert_allclose(
                        out, ref_out, rtol=1e-05, atol=1e-08
                    )


if __name__ == "__main__":
    unittest.main()
